{"id":1092,"date":"2020-05-29T07:42:06","date_gmt":"2020-05-29T07:42:06","guid":{"rendered":"http:\/\/janbosch.com\/blog\/?p=1092"},"modified":"2020-05-29T07:42:13","modified_gmt":"2020-05-29T07:42:13","slug":"why-youre-not-deploying-ai","status":"publish","type":"post","link":"https:\/\/janbosch.com\/blog\/index.php\/2020\/05\/29\/why-youre-not-deploying-ai\/","title":{"rendered":"Why you\u2019re not deploying AI"},"content":{"rendered":"\n
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Photo by Alexander Sinn on Unsplash\n<\/figcaption><\/figure>\n\n\n\n

Imagine the following scenario. A (sizable) team at a large company writes customer documents in response to customer requests. They request help from the automation team to reduce their repetitive tasks. The automation team brings in an AI company, which develops an ML model that generates the customer documents automatically and virtually eliminates the need for human involvement. The prototype works amazingly well and both the AI company and the automation team are eager to move it into production as the cost savings, as well as the speed and quality of response to customers, are bound to improve significantly.<\/p>\n\n\n\n

Sounds like a success story, right? Well, in this case, as well as in\n other cases that I\u2019ve seen, the company managed to grab defeat from the\n jaws of victory. The solution wasn\u2019t deployed. It\u2019s probably not the \nend of the story and hopefully, the solution will be rolled out in the \nfuture, but the company experiences a significant delay in reaping the \nbenefits from what should have been a straightforward and obvious \ndeployment.<\/p>\n\n\n\n

The pattern as I\u2019ve seen it is that if AI is used to improve some \nproduct capability and it doesn\u2019t affect existing organizational units \nnor existing processes, the deployment of the ML\/DL model is quick and \nfairly seamless. The moment, however, existing organizations or teams \nare threatened in their existence or asked to reduce significantly in \nsize or when existing work processes need to be adjusted to achieve the \nbenefits, things rapidly grind to a halt and many in the organization \nstart to backpedal.<\/p>\n\n\n\n

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Evolution stages of adopting ML\/DL<\/figcaption><\/figure>\n\n\n\n

In an earlier post<\/a>,\n I presented the stages that companies go through when adding ML\/DL to \nproducts. As shown in the figure, the first stage is experimenting &\n prototyping. Every company I work with has a host of those initiatives \nongoing. However, when looking to transition successful prototypes and \nproofs of concept to actual deployment, we run into roadblocks. advertorial \n<\/p>\n\n\n\n

The first and obvious roadblock is that you now need AI engineering \nto ensure that you have industry-strength, production-quality deployment\n of AI and, as I discussed in an earlier post<\/a>,\n that requires a set of solutions, architecture, infrastructure and \nprocesses that are often not recognized by data scientists and people \nwithout an engineering background.<\/p>\n\n\n\n

The second and more important roadblock is that the potential of AI \nis to significantly reduce cost while improving speed and quality. The \nfact is that for most companies, the primary cost driver is salaries. \nSo, to reap the benefits of AI, it means reallocating or releasing the \npeople that currently are doing the job that will be replaced by ML\/DL \nmodels.<\/p>\n\n\n\n

This is so obvious that it\u2019s almost painful to write it down and not \nfeel like an idiot, but I keep running into situations like the scenario\n that we started with. Everyone loves AI and it\u2019s on the top of the hype\n cycle. Everyone talks about all the great opportunities and benefits \nthat AI will bring to their organization and society at large. But when \nit hits close to home, the willingness to change and reap the benefits \nsuddenly is severely lacking.<\/p>\n\n\n\n

This is a problem as the competition isn\u2019t sitting still. We need to \ngo through the painful process of reaping the benefits by reducing cost,\n redesigning processes, reallocating people and aligning your \norganization with the benefits that AI can offer. As I wrote earlier<\/a>,\n it\u2019s not what AI can do for you; the question is how you redesign your \nentire organization, business models, products, customer engagement \nmodels and ways of working to align with digitalization, meaning \nsoftware, data and AI. This is the only way to capture the potential of \nAI to the full extent and the only way that you stay competitive in the \nlong run.<\/p>\n\n\n\n

In the startup community, large companies are often referred to as dinosaurs, ie slow, set in their ways and consequently ripe for disruption. Don\u2019t be a dinosaur!<\/p>\n\n\n\n

To get more insights earlier, sign up for my newsletter at <\/em>jan@janbosch.com<\/em><\/a> or follow me on<\/em> janbosch.com\/blog<\/em><\/a>, LinkedIn (<\/em>linkedin.com\/in\/janbosch<\/em><\/a>) or Twitter (<\/em>@JanBosch<\/em><\/a>).<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Imagine the following scenario. A (sizable) team at a large company writes customer documents in response to customer requests. They request help from the automation team to reduce their repetitive tasks. The automation team brings in an AI company, which develops an ML model that generates the customer documents automatically and virtually eliminates the need … Read more<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"generate_page_header":"","footnotes":""},"categories":[15,8,10],"tags":[],"_links":{"self":[{"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/posts\/1092"}],"collection":[{"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/comments?post=1092"}],"version-history":[{"count":1,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/posts\/1092\/revisions"}],"predecessor-version":[{"id":1094,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/posts\/1092\/revisions\/1094"}],"wp:attachment":[{"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/media?parent=1092"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/categories?post=1092"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/tags?post=1092"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}